Density functional theory study of the multimode Jahn-Teller effect – ground state distortion of benzene cation

The multideterminental-DFT approach performed to analyze Jahn-Teller (JT) active molecules is described. Extension of this method for the analysis of the adiabatic potential energy surfaces and the multimode JT effect is presented. Conceptually a simple model, based on the analogy between the JT distortion and reaction coordinates gives further information about microscopic origin of the JT effect. Within the harmonic approximation the JT distortion can be expressed as a linear combination of all totally symmetric normal modes in the low symmetry minimum energy conformation, which allows calculating the Intrinsic Distortion Path, IDP, exactly from the high symmetry nuclear configuration to the low symmetry energy minimum. It is possible to quantify the contribution of different normal modes to the distortion, their energy contribution to the total stabilization energy and how their contribution changes along the IDP. It is noteworthy that the results obtained by both multideterminental-DFT and IDP methods for different classes of JT active molecules are consistent and in agreement with available theoretical and experimental values. As an example, detailed description of the ground state distortion of benzene cation is given

Bves Modulates Tight Junction Associated Signaling

Blood vessel epicardial substance (Bves) is a transmembrane adhesion protein that regulates tight junction (TJ) formation in a variety of epithelia. The role of TJs within epithelium extends beyond the mechanical properties. They have been shown to play a direct role in regulation of RhoA and ZONAB/DbpA, a y-box transcription factor. We hypothesize that Bves can modulate RhoA activation and ZONAB/DbpA activity through its regulatory effect on TJ formation. Immortalized human corneal epithelial (HCE) cells were stably transfected with Flag-tagged full length chicken Bves (w-Bves) or C-terminus truncated Bves (t-Bves). We found that stably transfected w-Bves and t-Bves were interacting with endogenous human Bves. However, interaction with t-Bves appeared to disrupt cell membrane localization of endogenous Bves and interaction with ZO-1. w-Bves cells exhibited increased TJ function reflected by increased trans-epithelial electrical resistance, while t-Bves cells lost TJ protein immunolocalization at cell-cell contacts and exhibited decreased trans-epithelial electrical resistance. In parental HCE and w-Bves cells ZONAB/DbpA and GEF-H1 were seen at cell borders in the same pattern as ZO-1. However, expression of t-Bves led to decreased membrane localization of both ZONAB/DbpA and GEF-H1. t-Bves cells had increased RhoA activity, as indicated by a significant 30% increase in FRET activity compared to parental HCE cells. ZONAB/DbpA transcriptional activity, assessed using a luciferase reporter probe, was increased in t-Bves cells. These studies demonstrate that Bves expression and localization can regulate RhoA and ZONAB/DbpA activity

Practices participating in a dental PBRN have substantial and advantageous diversity even though as a group they have much in common with dentists at large

<p>Abstract</p> <p>Background</p> <p>Practice-based research networks offer important opportunities to move recent advances into routine clinical practice. If their findings are not only generalizable to dental practices at large, but can also elucidate how practice characteristics are related to treatment outcome, their importance is even further elevated. Our objective was to determine whether we met a key objective for The Dental Practice-Based Research Network (DPBRN): to recruit a diverse range of practitioner-investigators interested in doing DPBRN studies.</p> <p>Methods</p> <p>DPBRN participants completed an enrollment questionnaire about their practices and themselves. To date, more than 1100 practitioners from the five participating regions have completed the questionnaire. The regions consist of: Alabama/Mississippi, Florida/Georgia, Minnesota, Permanente Dental Associates, and Scandinavia (Denmark, Norway, and Sweden). We tested the hypothesis that there are statistically significant differences in key characteristics among DPBRN practices, based on responses from dentists who participated in DPBRN's first network-wide study (n = 546).</p> <p>Results</p> <p>There were statistically significant, substantive regional differences among DPBRN-participating dentists, their practices, and their patient populations.</p> <p>Conclusion</p> <p>Although as a group, participants have much in common with practices at large; their substantial diversity offers important advantages, such as being able to evaluate how practice differences may affect treatment outcomes, while simultaneously offering generalizability to dentists at large. This should help foster knowledge transfer in both the research-to-practice and practice-to-research directions.</p

The development of computational biology in South Africa: successes achieved and lessons learnt

Bioinformatics is now a critical skill in many research and commercial environments as biological data are increasing in both size and complexity. South African researchers recognized this need in the mid-1990s and responded by working with the government as well as international bodies to develop initiatives to build bioinformatics capacity in the country. Significant injections of support from these bodies provided a springboard for the establishment of computational biology units at multiple universities throughout the country, which took on teaching, basic research and support roles. Several challenges were encountered, for example with unreliability of funding, lack of skills, and lack of infrastructure. However, the bioinformatics community worked together to overcome these, and South Africa is now arguably the leading country in bioinformatics on the African continent. Here we discuss how the discipline developed in the country, highlighting the challenges, successes, and lessons learnt

An Improved, Bias-Reduced Probabilistic Functional Gene Network of Baker's Yeast, Saccharomyces cerevisiae

Background: Probabilistic functional gene networks are powerful theoretical frameworks for integrating heterogeneous functional genomics and proteomics data into objective models of cellular systems. Such networks provide syntheses of millions of discrete experimental observations, spanning DNA microarray experiments, physical protein interactions, genetic interactions, and comparative genomics; the resulting networks can then be easily applied to generate testable hypotheses regarding specific gene functions and associations. Methodology/Principal Findings: We report a significantly improved version (v. 2) of a probabilistic functional gene network [1] of the baker's yeast, Saccharomyces cerevisiae. We describe our optimization methods and illustrate their effects in three major areas: the reduction of functional bias in network training reference sets, the application of a probabilistic model for calculating confidences in pair-wise protein physical or genetic interactions, and the introduction of simple thresholds that eliminate many false positive mRNA co-expression relationships. Using the network, we predict and experimentally verify the function of the yeast RNA binding protein Puf6 in 60S ribosomal subunit biogenesis. Conclusions/Significance: YeastNet v. 2, constructed using these optimizations together with additional data, shows significant reduction in bias and improvements in precision and recall, in total covering 102,803 linkages among 5,483 yeast proteins (95% of the validated proteome). YeastNet is available from http://www.yeastnet.org.This work was supported by grants from the N.S.F. (IIS-0325116, EIA-0219061), N.I.H. (GM06779-01,GM076536-01), Welch (F-1515), and a Packard Fellowship (EMM). These agencies were not involved in the design and conduct of the study, in the collection, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript.Cellular and Molecular Biolog

A domain-based approach to predict protein-protein interactions

<p>Abstract</p> <p>Background</p> <p>Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins.</p> <p>Results</p> <p>DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms.</p> <p>Conclusion</p> <p>We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed using the DomainGA scores are reasonably low, and the erroneous predictions can be filtered further using supplementary approaches such as those based on literature search or other prediction methods.</p

The Microbiota Mediates Pathogen Clearance from the Gut Lumen after Non-Typhoidal Salmonella Diarrhea

Many enteropathogenic bacteria target the mammalian gut. The mechanisms protecting the host from infection are poorly understood. We have studied the protective functions of secretory antibodies (sIgA) and the microbiota, using a mouse model for S. typhimurium diarrhea. This pathogen is a common cause of diarrhea in humans world-wide. S. typhimurium (S. tmatt, sseD) causes a self-limiting gut infection in streptomycin-treated mice. After 40 days, all animals had overcome the disease, developed a sIgA response, and most had cleared the pathogen from the gut lumen. sIgA limited pathogen access to the mucosal surface and protected from gut inflammation in challenge infections. This protection was O-antigen specific, as demonstrated with pathogens lacking the S. typhimurium O-antigen (wbaP, S. enteritidis) and sIgA-deficient mice (TCRβ−/−δ−/−, JH−/−, IgA−/−, pIgR−/−). Surprisingly, sIgA-deficiency did not affect the kinetics of pathogen clearance from the gut lumen. Instead, this was mediated by the microbiota. This was confirmed using ‘L-mice’ which harbor a low complexity gut flora, lack colonization resistance and develop a normal sIgA response, but fail to clear S. tmatt from the gut lumen. In these mice, pathogen clearance was achieved by transferring a normal complex microbiota. Thus, besides colonization resistance ( = pathogen blockage by an intact microbiota), the microbiota mediates a second, novel protective function, i.e. pathogen clearance. Here, the normal microbiota re-grows from a state of depletion and disturbed composition and gradually clears even very high pathogen loads from the gut lumen, a site inaccessible to most “classical” immune effector mechanisms. In conclusion, sIgA and microbiota serve complementary protective functions. The microbiota confers colonization resistance and mediates pathogen clearance in primary infections, while sIgA protects from disease if the host re-encounters the same pathogen. This has implications for curing S. typhimurium diarrhea and for preventing transmission

Inhibition of glucose metabolism selectively targets autoreactive follicular helper T cells.

Follicular helper T (TFH) cells are expanded in systemic lupus erythematosus, where they are required to produce high affinity autoantibodies. Eliminating TFH cells would, however compromise the production of protective antibodies against viral and bacterial pathogens. Here we show that inhibiting glucose metabolism results in a drastic reduction of the frequency and number of TFH cells in lupus-prone mice. However, this inhibition has little effect on the production of T-cell-dependent antibodies following immunization with an exogenous antigen or on the frequency of virus-specific TFH cells induced by infection with influenza. In contrast, glutaminolysis inhibition reduces both immunization-induced and autoimmune TFH cells and humoral responses. Solute transporter gene signature suggests different glucose and amino acid fluxes between autoimmune TFH cells and exogenous antigen-specific TFH cells. Thus, blocking glucose metabolism may provide an effective therapeutic approach to treat systemic autoimmunity by eliminating autoreactive TFH cells while preserving protective immunity against pathogens

A proteome-wide protein interaction map for Campylobacter jejuni

'Systematic identification of protein interactions for the bacterium Campylobacter jejuni using high-throughput yeast two-hybrid screens detected interactions for 80% of the organism's proteins